Data Engineering & Analytics Infrastructure
Apache Spark
Distributed analytics engine with machine learning
- SF Score
- 8.3/10
- Pricing
- Free
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2 of 4 tools selected
Data Engineering & Analytics Infrastructure
Cloudera
Hybrid data platform for enterprise AI
- SF Score
- 8.2/10
- Pricing
- From $0.2/mo
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Compare features, strengths, trade-offs, and best-fit use cases.
Comparing
Where each one leads
Read straight from the published figures below — not a ranking, and not a recommendation.
Lowest starting price
Apache SparkFree to start
2 of 2 have a published entry price
Highest SF Score
Apache Spark8.3/10
2 of 2 are scored
Only one with a free plan
Apache SparkFree plan
free-plan status established for all 2
At a glance
The short version
The facts each tool publishes, lined up so you can scan down a column. Anything the catalog does not establish is marked as such rather than counted against the tool.
Apache Spark
- SF Score
- 8.3 / 10
- Starts at
- $0/mo
- Free plan
- Available
- Category
- AI Analytics Software › Data Engineering & Analytics Infrastructure
Cloudera
- SF Score
- 8.2 / 10
- Starts at
- $0.2/mo
- Free plan
- Not offered
- Category
- AI Analytics Software › Data Engineering & Analytics Infrastructure
Key differences
Where these tools actually diverge
The decision dimensions that separate this set, with each tool's own position stated plainly. No winner is declared — fit depends on your workflow.
Positioned for
Apache Spark
Data Engineering & Analytics Infrastructure
Cloudera
Data Engineering & Analytics Infrastructure
Pricing model
Apache Spark
Free
Cloudera
From $0.2/mo
SoftFinders Score
Apache Spark
8.3 / 10
Cloudera
8.2 / 10
Feature comparison
Capability matrix
Grouped by area. Every state carries a word as well as a colour, and anything the catalog has not established stays neutral rather than counting as a missing feature.
4 rows differ 1 identical
| Capability | | |
|---|---|---|
| Overview | ||
| SF Score | 8.3 / 10Value | 8.2 / 10Value |
| Pricing model | FreeValue | PaidValue |
| Starts at | $0/moValue | $0.2/moValue |
| Free plan | Yes | No |
| Category | AI Analytics Software › Data Engineering & Analytics InfrastructureValue | AI Analytics Software › Data Engineering & Analytics InfrastructureValue |
Scroll the matrix sideways to see every column.
Listed by one tool only
11 capabilities are published by a single tool in this set. That does not mean the others lack them — only that the catalog has not established an answer either way.
- Distributed ProcessingRun large jobs across compute clusters
- Spark SQLQuery structured data with SQL syntax
- MLlib LibraryBuild scalable machine learning pipelines securely
- Structured StreamingProcess streaming data with incremental computation
- GraphX AnalyticsAnalyze graph data at distributed scale
- Language SupportUse Python, Scala, Java, or R
- Data EngineeringBuild pipelines across hybrid data environments
- Lakehouse AnalyticsAnalyze managed data across open architectures
- AI WorkbenchDevelop machine learning models in governed workspaces
- AI InferenceDeploy models and generative AI securely
- Private AIKeep sensitive models and data controlled
Use cases
What each tool is built for
The workflows each tool publishes for itself. Cards appear only for tools that list use cases.
Apache Spark
- Process large datasets across compute clusters
- Build machine learning pipelines with MLlib
- Run structured streaming data transformations
- Query lakehouse tables using Spark SQL
- Support data engineering platform workloads
+3 more on the Apache Spark profile
Cloudera
- Run private AI across hybrid environments
- Build governed enterprise machine learning workflows
- Operate data engineering pipelines at scale
- Support regulated analytics and AI programs
- Modernize legacy Hadoop-based data estates
+1 more on the Cloudera profile
Pros & cons
Strengths and compromises
Each tool's published strengths and the compromises that come with them, as supplied — not scored or ranked against each other. Longer lists fold; nothing is left out.
Apache Spark
Pros
- Scales large data workloads across distributed clusters
- Supports multiple languages for analytical data workloads
- Open-source ecosystem reduces direct vendor lock-in risks
- Multiple cluster managers are officially supported
- Includes SQL ML and graph libraries
Cons
- Requires engineering expertise to operate well in production
- Not a business-user analytics application by default
- Cluster costs need careful ongoing management discipline
- Authentication is not enabled by default
- SparkR is deprecated in version 4.2
- Spark Connect differs from Classic execution semantics
Cloudera
Pros
- Strong fit for hybrid enterprise data estates
- Private AI capabilities support regulated enterprise programs
- Usage pricing gives clearer service-level cost signals
- Cloud supports AWS, Azure, and GCP
Cons
- Implementation requires experienced platform administration and governance
- Smaller teams may find it too heavy
- Usage-based costs need active governance and monitoring
- Data Visualization costs $2,000 per user annually
- GPU acceleration carries separate annual licensing
Pricing
What each one costs
Published pricing signals only. Where a tool has not established a price, that is what it says — never a zero and never "free".
Apache Spark
- Model
- Free
Open-source Apache project; infrastructure costs depend on hosting and managed services.
See full pricingCloudera
- Model
- Paid
Usage-based pricing varies by Cloudera service; AI inference and workbench have separate rates.
See full pricingNext step
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